code: def insight_1_feature_imp(self, df, classes=[0, 1]):
      #
      top_important_variables = []
      average_shap_values = []
  
      df_numpy = df.to_numpy()
  
      for i in range(1, 4):
          top_important_variables.append(df_numpy[-1 * (i + 1)][0])
          average_shap_values.append(df_numpy[-1 * (i + 1)][1])
  
      sentences = []
  
      sentences.append(
          "The top three features influencing the models predicted outcome are: {}, {}. {}".format(
              str(top_important_variables[0]),
              top_important_variables[1],
              top_important_variables[2],
          )
      )
  
      return sentences
  
file_path: explainx/lib/insight_classification.py
imports: from imports import *
label_desc: Use image node_id 5 for calling the insight_classification obj's underlying member method code with example usage: obj.insight_1_feature_imp(df, classes) and returns: sentences